Methods and systems for modeling using classification and regression trees

ABSTRACT

A method of valuation of large groups of assets using classification and regression trees is described. The method includes defining relevant portfolio segmentations, assessing performance of the classification and regression tree based model against a simple model and ranking all portfolio segments based upon performance of the models. Iterative and adaptive statistical evaluation of all assets and statistical inferences are used to generate the segmentations. The assets are collected into a database, grouped by credit variable, subdivided by ratings as to those variables and then rated individually. The assets are then regrouped and a collective valuation is established by cumulating individual valuations.

CROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. ProvisionalApplication No. 60/174,057, filed Dec. 30, 1999, which is herebyincorporated by reference in its entirety.

BACKGROUND OF THE INVENTION

[0002] This invention relates generally to valuation methods forfinancial instruments and more particularly to rapid valuation of largenumbers of financial instruments.

[0003] A large number of assets such as loans, e.g., ten thousand loansor other financial instruments, sometimes become available for sale dueto economic conditions, the planned or unplanned divestiture of assetsor as the result of legal remedies. The sale of thousands of commercialloans or other financial instruments sometimes involving the equivalentof billions of dollars in assets must sometimes occur within a fewmonths. Of course, the seller of assets wants to optimize the value ofthe portfolio, and will sometimes group the assets in “tranches.” Theterm “tranche” as used herein is not limited to foreign notes but alsoincludes assets and financial instrument groupings regardless of countryor jurisdiction.

[0004] Bidders may submit bids on all tranches, or on only sometranches. In order to win a tranche, a bidder typically must submit thehighest bid for that tranche. In connection with determining a bidamount to submit on a particular tranche, a bidder often will engageunderwriters to evaluate as many loans as possible within a tranche andwithin the available limited time. Up until the time for submitting abid is about to expire, the bidder will evaluate the loans underwrittenat that time, and then attempt to extrapolate a value to the loans thathave not then been analyzed by the underwriters.

[0005] As a result of this process, a bidder may significantlyundervalue a tranche and submit a bid that is not competitive or bidhigher than the underwritten value and assume unquantified risk. Ofcourse, since the objective is to win each tranche at a price thatenables a bidder to earn a return, losing a tranche due to significantundervaluation of the tranche represents a lost opportunity. It would bedesirable to provide a system that facilitates accurate valuation of alarge number of financial instruments in a short period of time andunderstand the associated probabilities of return for a given bid.

BRIEF SUMMARY OF THE INVENTION

[0006] In an exemplary embodiment, an iterative and adaptive approach isprovided wherein a portfolio is divided into three major valuations.Full underwriting of a first type of valuation of an asset portfolio isperformed based upon an adverse sample. A second valuation type isefficiently sampled from categories of common descriptive attributes,and the assets in the selective random sample are fully underwritten.The third valuation type is subjected to statistically inferredvaluation using underwriting values and variances of the first andsecond portions and applying statistical inference to individually valueeach asset in the third portion. Clustering and data reduction are usedin valuing the third portion.

[0007] As the process proceeds and more assets are underwritten, thenumber of assets in the first and second portions increase and thenumber of assets in the third portion decreases and the variance of thevaluation of the assets in the third portion becomes more and moredefined. More specifically, the assets in the third portion areevaluated by grouping the assets into clusters based on similarity tovaluations of assets in the first and second portions. Hypothetical bidsare generated using the valuations to determine an optimum bid withinparameters determined by the bidder. The optimum bid is identifiedthrough an iterative bid generation process.

[0008] One method for grouping assets based on similarity uses aclassification and regression tree analysis of asset portfolios, wherethe method includes the steps of defining relevant portfoliosegmentations, assessing performance of the classification andregression tree based model against a simple model and ranking allportfolio segments based upon performance of the models.

BRIEF DESCRIPTION OF THE DRAWINGS

[0009]FIG. 1 is a flow diagram illustrating a known process for valuinga portfolio of assets;

[0010]FIG. 2 is a flow diagram illustrating valuing a portfolio ofassets in accordance with one embodiment of the present invention;

[0011]FIG. 3 is a flow diagram illustrating, in more detail, oneembodiment of a first portion of a rapid valuation process for largeasset portfolios that breaks assets into categories of variance;

[0012]FIG. 4 is a flow diagram illustrating a second portion of a rapidvaluation process for a large asset portfolios that aggregates from abasis to a tranche or portfolio basis;

[0013]FIG. 5 illustrates a probability distribution for exemplary assetswhose recovery value is inferred;

[0014]FIG. 6 is a flow diagram of a supervised learning step of theprocess of FIG. 3;

[0015]FIG. 7 is a flow diagram of an unsupervised learning step of theprocess of FIG. 3;

[0016]FIG. 8 is an embodiment of the process for unsupervised learning;

[0017]FIG. 9 is an embodiment of the generation 1 (first pass) rapidasset valuation process;

[0018]FIG. 10 is a flow diagram of a fuzzy clustering method used in theunsupervised learning of FIG. 8;

[0019]FIG. 11 is a pair of tables showing an example of model selectionand model weighting for a rapid asset evaluation process;

[0020]FIG. 12 is a table showing exemplary attributes for a rapid assetvaluation process; and

[0021]FIG. 13 is a cluster diagram of an exemplary clustering method fora rapid asset valuation process; and

[0022]FIG. 14 is a computer network schematic.

DETAILED DESCRIPTION OF THE INVENTION

[0023]FIG. 1 is a diagram 10 illustrating a known process for valuing alarge portfolio of assets 12 through an underwriting cycle and throughto making a bid for purchasing asset portfolio 12, for example, in anauction. FIG. 1 is a high level overview of a typical underwriting andextrapolation process 10 which is not iterative and not automated. Indiagram 10, underwriters underwrite 14 a number of individual assetsfrom portfolio 12 to generate an underwritten first portion 16 and anuntouched remainder portion 18. Before any of the assets areunderwritten, first portion 16 is zero percent and remainder portion 18is one hundred percent of portfolio 12. As the underwriting processprogresses, first portion 16 increases and remainder portion 18decreases. The objective is to underwrite as many assets as possiblebefore a bid is submitted for the purchase of asset portfolio. The teamof underwriters continues individually underwriting 14 until just beforea bid must be submitted. A gross extrapolation 20 is made to evaluateremainder portion 18. The extrapolated value 20 becomes thenon-underwritten inferred value 24. The gross extrapolation generates avaluation 24 for remainder portion 18. Valuation 22 is simply the totalof the individual asset values in first portion 16. However, valuation24 is a group valuation generated by extrapolation and may be discountedaccordingly. Valuations 22 and 24 are then totaled to produce theportfolio asset value 26. Valuation processes are performed on eachtranche of the portfolio.

[0024]FIG. 2 is a diagram illustrating one embodiment of a system 28 forrapid asset valuation. Included in FIG. 2 are representations of processsteps taken by system 28 in valuating asset portfolio 12. System 28individually evaluates (“touches”) every asset, except for a very smallquantity 30 of untouched assets considered statistically insignificantor financially immaterial. Specifically, all assets in portfolio 12other than quantity 30 undergo an iterative and adaptive valuation 32 inwhich the assets in portfolio 12 are individually valued, listedindividually in tables and then selected from the tables and groupedinto any desired or required groups or tranches for bidding purposes (asdescribed below.) As in diagram 10, underwriters begin a full underwrite14 of individual assets in portfolio 12 to produce a fully underwrittenfirst portion 16 of assets. Underwriters also underwrite 34 a sample ofassets in a second portion 36 of portfolio 12, and a computer 38statistically infers 40 value for a third portion 42 of portfolio 12.Computer 38 also repetitively generates 44 tables (described below)showing values assigned to the assets in portions 16, 36 and 42 asdescribed below. In one embodiment, computer 38 is configured as a standalone computer. In another embodiment, computer 38 is configured as aserver connected to at least one client system through a network (shownand described in FIG. 14), such as a wide-area network (WAN) or alocal-area network (LAN).

[0025] For example, and still referring to FIG. 2, an unsampled andnon-underwritten portion 46 of a third portion 42 of portfolio 12 issubjected to a statistical inference procedure 40 using fuzzy-C meansclustering (“FCM”) and a composite High/Expected/Low/Timing/Risk(“HELTR”) score to generate two categories 48 and 50. HELTR is definedas H—High cash flow, E—Expected cash flow, L—Low cash flow, T—Timing ofcash flow (for example in months: 0-6, 7-18, 19-36, 37-60), and R—Riskassessment of borrower (9—boxer used by credit analysts). Category 48 isdeemed to have sufficient commonality for evaluation as a whole.Category 50 is further divided into clusters 52 and 54 that are, inturn, further subdivided. Cluster 52 is divided into subclusters 56 and58, while cluster 54 is subdivided into subclusters 60, 62 and 64.Cluster and subclusters are shown both in a “tree” chart 66 and as boxesin valuation block 68. These individual asset values are then regroupedinto tranches 70, 72 and 74 for bid purposes. Any number of tranchescould be assembled in any arrangement set by the seller.

[0026] Individual asset data (not shown) for each asset in portfolio 12is entered into a database 76 from which selected data 78 is retrievedbased on a given criteria 80 for the iterative and adaptive process 32.When criteria 80 is established for valuation of any asset, thatestablished criteria 80 is stored in database 76 for use in valuatingother asset data in database 76 which shares such an establishedcriteria. Iterative and adaptive valuation process 32 thus develops 82valuations (described below) and groups 84 them for use in bidding.

[0027]FIGS. 3 and 4 together form a flowchart 85 illustrating afunctional overview of one embodiment of system 28 (shown in FIG. 2) forevaluation of a large asset portfolio 12. Valuation procedures 14, 34and 40 (see also FIG. 2) are simultaneously and sequentially used insystem 28 in a manner described below. As described above, fullunderwriting 14 is a first type of valuation procedure. Grouping andsampling underwriting 34 with fall underwriting of the samples is asecond type of valuation procedure. Statistical inference 40 is a thirdtype of valuation procedure, which is an automated grouping andautomated valuation. Procedures 14, 34 and 40 are based on objectivecriteria established as described below.

[0028] “Underwriting” as used herein means a process in which a person(“underwriter”) reviews an asset in accordance with establishedprinciples and determines a current purchase price for buying the asset.During underwriting, the underwriter uses pre-existing or establishedcriteria 80 for the valuations. “Criteria” means rules relevant to assetvalue and a rating based on such categories. For example, as a criteria,an underwriter might determine three years of cash flow history of theborrower to be a category of information relevant to asset valuation andmight give a certain rating to various levels of cash flow.

[0029] Full underwriting 14 is done in two ways, a full cash basismanner 86 and a partial cash basis manner 88. Both full cash basismanner 86 and partial cash basis manner 88 start with sets 90 and 92 ofassets that are fully individually reviewed 14 (see FIG. 2). Such fullreview 14 is usually due to the large dollar, or other appropriatecurrency, amounts of the assets being reviewed relative to other assetsin the portfolio or due to the borrower being so well known or soreliable that the assets can be quickly and reliably fully underwrittenor the assets are marked to market such that there is very littlevariance associated with the value of said assets. Asset set 90 isevaluated by underwriters 94 and each asset in set 90 receives avaluation with very little variation such as an asset backed with cashor a tradable commodity with full cash value and is placed in a fullvalue table 96. Selected individual values for assets in table 96 arestored as a fully underwritten group value 98.

[0030] Set 92 is evaluated by a team of underwriters 100, which could bethe same as team 94, but each asset receives a discounted or partialvalue and is placed in a partial value table 102. Selected individualvalues for assets in a tranche in table 102 are stored as a partialvalue fully underwritten group value 104. Criteria 80 (shown in FIG. 2)for full cash basis manner 86 and partial cash basis manner 88 arestored in database 76 (shown in FIG. 2) in a digital storage memory (notshown) of computer 38 (shown in FIG. 2) for use in supervised learning206 and unsupervised learning 208 of automated valuation 40.

[0031] Sampling underwriting 34 is accomplished using two procedures, afull sampling 106 procedure and a partial sampling 108 procedure. Fullsampling 106 is utilized for categories of large assets and includes aone hundred percent sampling 110 of the sample groups in the categoriesof assets being sampled. The assets in full sampling 106 are notindividually underwritten but rather are underwritten in full samplinggroups 112 based on a determined commonality. A resulting full samplinggroup valuation (not shown) is created and then desegregated based on arule 114 to generate an individual full sample asset value table 116.Individual full sample asset values in table 116 are then uploadedelectronically into any full sampling group valuation 118 required forbidding as suggested by the grouping of assets in a tranche. The numberof assets in an underwriting sample grouping can be as little as one toany number of assets. Partial sampling 108 is for medium categories ofassets and includes forming a cluster sample group 120 by one hundredpercent sampling of a representative group from within a cluster of thegroups being sampled and random sampling of the other groups in thecluster. In partial sampling 108, all groups are sampled, but some arepartly valued by extrapolation from cluster sample group 120. Partialsampling 108 includes an asset level re-underwrite 122 with manual dataentry 125 to produce an alpha credit analyst table 126 which is given anasset class adjustment 128 to produce an adjusted credit analyst table130. As described above, individual assets are selected from adjustedcredit analyst table 130 according to tranche grouping to produce apartial sampling credit value 132 for use in bidding on tranche 70(shown in FIG. 2).

[0032] Automatic valuation procedure 40 utilizes supervised learningprocess 206, an unsupervised learning process 208 and an upload from astatistical inferencing algorithm 134 to generate an underwritingclusters table 136 which is stored in a digital storage device. Insupervised learning process 206, an experienced underwriter who knowswhat questions to ask to establish value, assists the computer indetermining whether or not an asset is a good investment and how tovalue the asset. In unsupervised learning process 208, the computersegments and classifies assets and objectively self-evaluates the assetsbased on feedback from the data. An underwriter periodically reviews theunsupervised learning process 208 to determine whether the computer ismaking sensible underwriting conclusions. The computer uses statisticalalgorithms 134 to make its inferences. For example, but not by way oflimitation, one embodiment uses the Design For Six Sigma (“DFSS”)quality paradigm developed and used by General Electric Company andapplied in a Due Diligence (“DD”) asset valuation process using amulti-generational product development (“MGPD”) mode to value the assetdata with increasing accuracy. Learning processes 206 and 208incorporate the accumulated knowledge as the valuation progresses intocash flow recovery and probability of recovery calculations on anongoing, real time basis. Supervised learning process 206 uses businessrules to identify clusters of assets having common aspects for valuationpurposes. Unsupervised learning process 208 uses feedback from priordata valuations performed by procedure 40 to determine if progress isbeing made with respect to increasing valuation confidence.Identification of all available raw data and discovery ofinterrelationships of clusters of these available raw data is possibledue to the use of high-speed computers, as is described below.

[0033] In one exemplary embodiment, a fuzzy clustering means (“FCM”)process of unsupervised organization of raw data using a HELTR scoringtechnique is employed to infer valuations of credit scores onto assetsin portfolios, as described below. Such clustering techniques have beendeveloped in response to more sophisticated classification segments todescribe assets and high asset counts in portfolios that must beassessed in time periods that do not allow manual processing.

[0034] One exemplary method first organizes valuation scores (staticand/or probabilistic recoveries) in a computerized system. Adjustmentsare then made to the valuation scores for special factors and businessdecisions. Then a reconciliation of multiple valuation scores describingthe same asset and an overall adjustment to interview/override theinferred valuation is performed.

[0035] Organizing valuation scores is performed by collating, inelectronic form, a cluster number, a cluster name, descriptiveattributes of the cluster(s), probabilistic recovery values (anillustrative example is a HELTR score) and the underwriter's confidencein each cluster's valuation based upon the strengths of each cluster'sdescriptive attributes. The cluster number is a unique identifier of aspecific set of descriptive attributes that are facts about an assetwhich a person skilled in evaluations uses to assess value of an asset.Examples of descriptive attributes include, but are not limited to,payment status, asset type, borrower's credit worthiness expressed as ascore, location and seniority of a claim. The cluster name is, in oneembodiment, an alpha-numeric name that describes the cluster'sdescriptive attributes or sources. One example of descriptive attributesis found in FIG. 12, described below.

[0036] Descriptive attributes are the facts or dimensions or vectorsthat were used to develop the asset's value. Computer logic is used tocheck for replicated clusters, if any, and alert the analysts orunderwriters.

[0037] Because each asset can be described by many combinations ofdescriptive attributes, various levels of value for the same asset mayoccur. Probabilistic recovery values or credit score or any numericalindication of the asset's worth are indicators of worth designated atthe discrete asset level. All of the information from the variousdescriptive attributes is synthesized such that a purchase or sale pricecan be ascertained as a fixed value or a probabilistic one. Anillustrative embodiment used herein is the HELTR score. Each cluster hasa unique set of descriptive attributes and designated HELTR score.

[0038] Every cluster's unique attributes contribute to a valuation ofcluster value. Different combinations of attributes provide a higherconfidence or confidence interval of a particular cluster's score. Forexample, if any asset was described as a green piece of paper withheight equal to 2.5″ and width equal to 5″—one might ascribe a value of0 to 1000 dollars and place very little confidence in this assessment.If this same asset was described with one more fact or attribute orvector as being a real $20 US bill, one would place a very highconfidence factor on this cluster value of $20 US dollars.

[0039] A cluster's valuation and confidence is determined at a point intime and recorded. Sometimes new information becomes available and theanalyst would like to alter the value(s). The value is altered manuallyor automatically with a data field and decision rules, in the automatedfashion via computer code. The prior values are manipulated to reflectnew information. As an illustrative example, assume the prior clusterconfidence was recorded at 0.1 and it is learned that a different assetwith exact descriptive attributes as in this cluster just sold for overthe predicted “most probable” value. Rules were in effect such that ifthis event occurred, cluster confidence is multiplied by 10. 0.1×10=1which is the revised cluster confidence.

[0040] The purpose of such a process is to reconcile multiple scores forthe same asset, controlling for the confidence associated with eachsource of valuation of each dimension of valuation. Using the HELTR asan illustrative example with sample data points on a particular asset:Cluster Cluster Valuative Number Name High Exp Low Time Confidence HighExp Low Time 1 Lien 85 62 .15 3 3   (.3/1.65)(.85) (.3/1 65)(.62) (3/1.65)( 15) (.3/1.65)(3) positions - recourse 2 Asset 45 .4 .31 3 7  (.7/1 65)( 45) ( 7/1.65)(.4) ( 7/1 65)( 31) ( 7/1 65)(3)classification - industry - age 3 Coordinates - .9 .5 .2 2 65    (65/1.65)( 9) (.65/1.65)(.5) (.65/1 54)( 2) ( 65/1.65)(2) use - borrowern x 1 65 6999 .4792 .2374 2.6059

[0041] The cluster consensus valuation is a high value of 0.6999, mostlikely 0.4792, low 0.2374 with a timing of 2.6059. Different logic canbe applied to manipulate any of the weights.

[0042] The consensus scores are developed in the context of globalassumptions. Should a global assumption change occur, process steps 128,138 are included in the methodology to weight the consensus score.Illustrative examples are fraud discovery in certain valuation factors,macroeconomic changes, fangible market value established for an assetclass, and loss of or increase of inferenced asset valuationmethodologies relative to other methodologies being employed.

[0043] In another embodiment, a cross correlation tool is used toquickly understand and describe the composition of a portfolio.Typically, the tool is used to correlate a response of a user selectedvariable versus other variables in an asset portfolio. The tool quicklyidentifies unexpectedly high or low correlation between two attributevariables and the response variable. Attribute variables are of twotypes, continuous and categorical. The cross correlations are computedby the correlation tool between all variables of interest and their binor level and presented, in one embodiment, in a two dimensional matrixfor easy identification of trends amongst the assets in the portfolios.

[0044] First, the cross-correlation tool identifies attribute variablesin the portfolio of assets as one of continuous or categorical. For eachvariable aggregation levels are computed by bins for continuousvariables and by value for categorical variables.

[0045] A user looking to identify correlations with the tool will selecta response variable, Y_(r), for example, an expected recovery or count.For all combinations of pairs of attribute variables (x1 and x2) andtheir levels (a and b), compute the average value of the responsevariable, Y_(r), according to:

Y _(r)=sum(Y(x1=a and x2=b))/count(x1=a and x2=b).

[0046] An expected value, Y_(expect), of the response variable iscalculated according to:

Y_(expect)=(sum(Y(x1=a))*count(x1=a)+sum(Y(x2=b))*count(x2=b)))/(count(x1=a)*count(x2=b)).

[0047] A deviation, Y_(error), of the chosen response variable, Y_(r),from the expected value, Y_(expect), using weighted values of occurrenceof x1=a and x2=b separately, is calculated by:

Y _(error) =Y _(r) −Y _(expect).

[0048] In one embodiment, expected values and deviations are displayedin multi-dimensional displays to make variations from expected valueseasy to identify.

[0049] In another exemplary embodiment, a transfer function process thatconverts raw data into the ultimate bid price is used, as describedbelow. Table 136 is electronically adjusted using modified coefficientsdeveloped in procedures 14, 34 and 40 to give a coefficient adjustmentto a credit score 138 for the asset and to generate an adjusted creditanalyst table 140 of inferred individual asset credit values. Individualasset values are taken from table 140 as required by tranche grouping togenerate an inferred credit valuation 142. Finally an extrapolation ismade on the negligible remainder 30 of “untouched” assets to generate atable of untouched assets 144. Values from table 144 are selected togenerate an untouched asset valuation.

[0050] Full cash valuation 98, partial cash valuation 104, full samplingcredit valuation 118, partial credit values 132, inferred credit value142 and any value assigned from untouched asset table 144 are cumulatedand are mutually exclusive with the priority being full cash valuation98 to inferred credit value 142 consecutively. A sum of the valuationsrepresents value of the portfolio.

[0051]FIG. 4 is a flow diagram of a bid preparation stage 168 performedby system 28 (shown in FIG. 2). The cumulated valuations 98, 104, 118,132, 142 and to 144 are combined in a risk preference loan levelvaluation step 146. A deterministic cash flow bridge 148 is producedusing a cash flow timing table 150 to develop a stochastic cash flowbridge 152. A stochastic or probabilistic cash flow bridge 152 iscreated and used to determine a proposed tranche bid price 154 to whichis applied a tranche model 156 iteratively until a certain threshold 158is reached. Threshold 158 is, for example, an internal rate of return(“IRR”) greater than some value, a certain time to profit (“TTP”), and apositive net present value (“NPV”).

[0052] In general, NPV is defined as: $\begin{matrix}{{NPV} = {c_{0} + \frac{c_{1}}{1 + r}}} & \text{(Equation~~A)}\end{matrix}$

[0053] where C₀ is the investment at time 0, C₁ is the expected payoffat time 1, and r is the discount factor. The basic idea is that a dollartoday is worth more than a dollar tomorrow.

[0054] In the case of insurance policies, NPV is defined as:$\begin{matrix}{{NPV} = {{\sum P} - {\sum E} - {( {\sum C} ) \times \frac{A}{E_{w}}}}} & \text{(Equation~~B)}\end{matrix}$

[0055] where P is the premium, E is the expected nominal cost, and C isthe claim cost. In essence, Equation B is how net income as thedifference of profit and weighted expected risk is generated. Note thatthe summation is summing across all the policies in a specific segment.Also note that all the premium, nominal cost, and claim cost have beendiscounted before entering the equation. As a result, a profitabilityscore is generated.

[0056] If threshold conditions 160 are met, bid 154 is subjected to asimulated bid opening analysis 161 to predict whether the bid can beexpected to be a winning bid. An outcome of a sealed bid auction dependson sizes of the bids received from each bidder. Execution of the auctioninvolves opening all of the bids and selling the items up for auction tothe highest bidder. In traditional sealed bid auctions, bidders are notallowed to change their bids once their bid is submitted and bidders donot know the bids placed by other bidders until the bids are opened,making the outcome of the auction uncertain. By placing higher bids, aprobability that the auction will be won is higher, but value gain islower if it was possible to have won the auction at a lower price.

[0057] Simulating competitive bidding increases the probability ofcapturing the highest upside of profitability by setting a range ofbid/sale prices that have a propensity to exhaust any competing bidder'spurses before ones own purse such that the most desirable assetstransact with the highest preservation of capital. Pricing decisions arebrought into focus by an analytically robust process because pureanecdotal business judgment can be augmented by a data driven approachnot subject to a hidden agenda, personality or unilateral knowledge.

[0058] Each potential bidder has a range of possible bids that might besubmitted to a sealed bid auction. The range of bids can be expressed asa statistical distribution. By stochastically sampling from adistribution of bid values, one possible auction scenario may besimulated. Further by using an iterative sampling technique, for examplea Monte Carlo analysis, many scenarios are simulated to produce adistribution of outcomes. The distribution of outcomes include aprobability of winning the auction item(s) and the value gain. Byvarying the value of ones own bid, a probability of winning the auctionagainst ones own bid price can be determined.

[0059] The following core elements are used to simulate a competitivebidding yield, codification of market rules and contracts intocomputerized business rules, codification of potentialcompetition/market forces, forecasted budgets and priorities into apreference matrix, one's own bidding capacity, preferences, risk/returntradeoffs agreed to codified into a preference matrix, and acomputerized stochastic optimization.

[0060] Analysis 160 simulates a competitive environment with othercompanies having various financial capabilities bidding against the bidscalculated by system 28. In one embodiment, analysis 160, for exampleand without limitation, includes a total bid limit such as would be thecase where the total value of the assets exceed the financialcapabilities of the entity using system 28. In one embodiment, analysis160 might assess the profitability, in such case of limited resources tobid, of bidding on various combinations of tranches. Analysis 160 alsotakes into account past history in bidding against known competitors andinformation on the various types of assets preferred by competingbidders. In analysis 160, the tranche bid is then evaluated and set bymanagement 162 and a final tranche bid 164 made. All valuations prior tothe making of the bid 164 can be repeated as desired. Further, since theprocess is self-adjusting and iterative, the tranche bid price 164 tendsto climb upward with each iteration as more and more value is found bythe iterations performed by system 28.

[0061] The process described by flowchart 85 includes an evaluationstage 166 (shown in FIG. 3) and a bid preparation stage 168 (shown inFIG. 4). Evaluation stage 166 includes procedures 14, 34 and 40.Evaluation stage 166 runs constantly until stopped, with the automaticvaluation procedure 40 and sampling procedures 34 attempting to findextra value in various assets or categories of assets.

[0062] Referring once again to FIG. 2, and in accordance with rapidasset valuation, data categories 170, 172 and 174 within the assets ofportfolio 12 are identified on each asset and stored in database 76.Iterative and adaptive valuation process 32 takes portions of selecteddata 78 and applies criteria 80 to the portions of selected data 78 in astatistical manner to increase the known asset value rather than theasset value being a gross extrapolation 20. In accordance with method 28the assets are divided into at least first portion 16, second portion 36and third portion or remainder 42. Using procedure 14, the assets inportion 16 are fully underwritten to determine valuation 98 and partialvalue fully underwritten valuation 104 and to establish criteria 80 forsuch valuation. Using procedure 34, process 28 samples a quantity ofassets from second portion 36 representative of groups in second portion36 to determine full sampling group valuation 118 and partial samplingcredit values 132 for second portion 36 and to establish additionalcriteria 80 for such valuation. Using procedure 40, partially supervisedlearning process 206 and partially unsupervised learning process 208 areperformed by an automated analyzer such as computer 38 of FIG. 2. Inorder to learn, the automated analyzer extracts established criteria 80and selected data 78 as to third portion or remainder 42 and dividesthird portion 42 into portions 46, and then further divides each portion46 into categories 48 and 50 and category 50 into clusters 52, 54 andclusters 52, 54 into subclusters 56, 58, 60, 62 and 64 using criteria 80imported from database 76 and each of processes 206 and 208. Individualasset valuations are established for the assets in subclusters 56, 58,60, 62 and 64 by statistical inference.

[0063] The individual asset valuations are listed in cluster tables 136(see FIG. 3) and after adjustment 138, listed in a credit analyst table140. The established criteria 80 are objective since criteria 80 comefrom database 76 where they have been placed during full underwritingprocedure 14 and sample underwriting procedure 34. In other words,information obtained in full value table 96, partial value table 102,table 116, alpha credit analyst table 126, adjusted credit analyst table130, adjusted credit analyst table 140 and untouched asset table 144 forall assets is placed into database 76 in a digital storage device, suchas the hard disk storage 178 of computer 38, and correlations are madeby procedure 40 with criteria 80 from procedures 14 and 34. Duringprocedure 40, criteria 80 which are of statistical significance with anacceptable degree of reliability, are entered. That is, procedure 40iteratively learns as it values and establishes criteria 80. Supervisedlearning process 206 and unsupervised learning process 208 increase theaccuracy of statistically inferred valuation 142 by correlating toestablished criteria 80 in database 76 on assets in fully underwrittenfirst portion 16 and assets in sample underwritten second portion 36.Selected data 78 related to one or more assets in third portion 42similar to selected data 78 on assets in portions 16 and/or 36 arelocated in database 76 and then by statistical inference, a value foreach asset in third portion 42 is determined from the locatedinformation.

[0064] During the process described by flowchart 85, assets are valuedat an individual asset level, and the individual asset values aretabulated or grouped in one or more combinations. To have maximumflexibility for various bidding scenarios, any subset of portfolio 12 isvalued and priced separately in a particular time frame. In knownprocess 10, if a seller of assets regroups the assets, for example fromgroupings by asset company to groupings by geographical location ofborrowers, revaluation of bids may be inadequate because grossextrapolation 20 will need to be performed. In using system 28, becauseindividual asset values are developed and listed in tables 96, 102, 116,130, 140 and 144, these values can be electronically regrouped intodifferent valuations 98, 104, 118, 132, 142 whose “food chain” selectioncriteria is mutually exclusive and selectable by the analysts conductingthe evaluation and is further described below. If the seller groups theassets, then grouping according to seller groups or tranches is easilymade and an appropriate valuation 146 developed for that tranche. Theindividual asset values are thus easily regrouped for third portion 42to objectively obtain an inferred valuation 142 for that group ortranche.

[0065] Many methods may be employed to establish asset value. Dependingupon the objectives of the valuation, the relative merits of differentvaluation methodologies establish the desirability of the valuationtechniques for a particular asset. One methodology is similar to a “foodchain” which preserves assumption development methods yet selects theintervals with the highest confidence intervals.

[0066] In one introductory illustrative example of a food chain, one mayprefer to value a financial asset more by what similar assets trade inthe open market for versus an individual's opinion. In rank order, themarket-to-market value is selected over an individual's opinion.

[0067] In the same way assets in a portfolio with a forecasted cash flowrecovery may be evaluated by a number of valuation techniques. Thetypical objective is to establish, with as high a probability available,what the future cash flow will be. The valuation methodologies areranked in order of their capability to accurately quantify cash flow, orcash equivalent, forecasts with the least downside variances and/ormaximum upside variances. The asset is valued by all available methodsthat have merit, or may have business logic rules to eliminate duplicatework when it is known that more accurate methods will preclude the needto assess an asset's valuation once the best method has been employed.

[0068] In order to provide the best forecast of asset value, assets areevaluated by each method within a food chain until such time as they arevalued by the best available method for each particular asset. Once thisbest value is found, the asset is said to have its value, irrespectiveto other values lower (with more variance) in the food chain and is sentto the completed state.

[0069] As an example, a portfolio of assets is evaluated using a foodchain. The first valuation method in the food chain is the one whichmost closely matches the valuation objectives—namely to find the valuewith the highest degree of accuracy (tightest confidence interval). Assoon as the asset is valued by a methodology for which a value wasestablished for that unique asset, it is sent to the valuation table andremoved from any further steps in the food chain. A list of assets fromthe original portfolio that did not match any valuation methods is keptin the untouched asset table. The objective is to drive this untouchedtable to zero assets.

[0070] One example of a food chain is as follows, in order ofpreference. (a) 100% cash in hand for the asset, (b) partial cash inhand for the asset, (c) liquid market value for like asset, (d)directunderwrite, and (e) inferred underwrite.

[0071] The food chain approach provides an ability to find the bestprobability distribution shape, reduces probability distributionvariance (especially on the downside tails), provides capability toestablish probability distributions quickly while preserving allavailable knowledge in the constituencies and provides the ability toprovide the best estimate of value at any point in the discoveryprocess.

[0072] As shown in FIG. 4, the general framework of bid preparationstage 168 is to price bid 164 similar to option valuation paradigmswhere the winning investor will have the right, but not the obligation,to recover the investment. The values are desegregated into three partsfor each tranche, a time value of money component, an inherent valuecomponent and a probable cash flow component. The time value of moneyand the inherent value are deterministically calculated and have littlevariation once established. The time value of money is computed bytaking a firm's cost of capital for a low risk investment multiplied bythe investment for the applicable period which represents an opportunityfor alternate investment that is foregone in order to make the presentinvestment. Inherent value is a known liquid asset value, which is inexcess of the purchase price and is available immediately after takingcontrol of the assets. One embodiment is a well traded securitypurchased below market value as part of a portfolio. Probable cash flowvariance is a function of the assumptions a due diligence team makes andthe process it selects to convert raw data into a cash flow recoverystream. The systems described herein are configured to reduce negativevariances and find value.

[0073]FIG. 5 is a triangular probability distribution graph for atypical minimum three-point asset evaluation 180. In accordance withprocess 40 a minimum of three cases per financial instrument areevaluated. A vertical axis 182 denotes increasing probability and ahorizontal axis 184 denotes increasing portion of recovery. Aliquidation or worst case percentage 186 of a face value line 188, abest case percentage 190 of face value 188, and a most probable casepercentage and recovery value 192 of face value 188 are shown. Theprobability of worse case percentage 186 is zero, the probability ofbest case scenario 190 is zero and a probability 194 of the mostprobable percentage 192 of recovery is a value represented by point 196.The size of an area 198 under a curve 200 defined by a line connectingpoints 186, 196 and 190 is representative of value in the asset. Thenotational asset value holds to an area 202 of a rectangle bounded by a100% probability line 204 of a 100% recovery of face value 188 is ameasure of the portion of face value 188 that can be attributed to theasset represented by curve 200. Points 186, 196 and 190 and lines 188and 204, and thus areas 198 and 202, will vary depending on selecteddata 78 chosen for the asset in question and criteria 80 applied to theasset and ascribed probabilities of asset value recovery. Horizontalaxis 184 can be expressed in currency units (e.g. dollars) rather thanpercentage of face value. When currency units are used, areas 198 undercurves 200 for different assets will be in currency units and thus areas198 relate to each other in magnitude and hence in significance tooverall bids 70, 72 and 74. The more that is known about the asset, themore curve 200 can be refined. Statistics are applied to curve 200 ascriteria 80 are established to help establish the location of points186, 196 and 190 and hence area 198 and thus the expected value of theasset. The timing of cash flows, which affects value, can be based uponhistogram results of the timing attributes.

[0074] For example, the cash flow recovery timing can be broken downinto three bins of 0-6 months, 7-12 months, 13-18 months, and so on. Theautomated analyzer 38 using algorithm 134 can select the bin width basedupon a sensitivity study trade off of timing to valuation against thegauge recovery and rate determined possible by an underwriter. In anexemplary embodiment, a minimum of 4 bins should be utilized when thediscount factor is more than 25%. For a discount factor between 10 and25, a minimum of 6 bins should be used to cover the likely recoveryperiods.

[0075] In accordance with procedure 40 other sources of data are chosenthat an underwriter would be able to utilize to assess value in afinancial instrument. Criteria 80, established by underwriting teams 94,100 114, 122 and 140 in procedures 14 and 34, are useful in that regard.In accordance with the process described by flowchart 85, raw data isturned into a recovery and a rule set is selected to apply a valuationto the raw data and this rule set is coded into the valuation databasein the form of criteria 80. Each time a cluster is touched by multiplehits during a valuation in procedures 14, 34 or 40, a consensus forecastis developed and applied to the cluster. In accordance with system 28,the probability distributions of cash flows and timing at the tranchelevel is determined by developing valuation transfer function 146 at theasset level which will take raw data, rationalize the assumptions thatdata will generate and aggregate the valuations of the individual assetsin the tranche.

[0076] Since all recoveries are not homogeneous, a method to establishthe variability of cash flow recoveries is provided. Individual assetsare clustered by group exposure. As much face value as possible istraditionally underwritten in the time permitted, recognizing that asizable sample remains for clustering. Clustering reserves are estimatedusing a sample size equal to one hundred forty five plus 2.65% of theface count and a regression analysis of variance. This produces samplesizes of thirty for a face count of 100 assets, 150 for a face count of1,000 assets, 400 for a face count of 5,000 assets, 500 for a face countof 10,000 assets, and 600 for a face count of 20,000 assets.

[0077] During statistical inference procedure 40, assets remaining inthird portion 42 of portfolio 12 are clustered by descriptiveunderwriting attributes or criteria 80 and random samples are taken fromeach cluster and the sample underwritten. In one embodiment, samplingfrom a cluster in procedure 40 is stopped when asset level mean variancefalls below 10%. In another embodiment, sampling is stopped when tranchelevel mean variance falls below 15%. Portfolio mean variance is not usedas a stop point if the potential unit of sale is less than the entireportfolio. In accordance with procedure 40, recovery valuation of thecluster sampling is inferred onto the corresponding cluster population.In using system 28, the goal is to touch each inferred asset valuationvia three or more unique clusters. During procedure 40 a cluster'sunderwriting confidence and descriptive attribute's relevance isweighed.

[0078] By way of example, without limitation, 0=no confidence that thiscluster's descriptive attributes will provide a meaningful valuation;1=complete confidence that this cluster's descriptive attributes willprovide as accurate of a valuation as individually underwriting eachinstrument, and numbers between 1 and 0 indicate partial confidence inthe valuation. Reconciliation of these values occurs within adjustedcredit analyst table 130. In procedure 40 cash flow at asset level isthen adjusted by macroeconomic coefficients within adjusted creditanalyst table 140. Macroeconomic coefficients are, in one embodiment,associated with major asset classes such as for example, withoutlimitation, real-estate residential loan or commercial equipment loan.The coefficients can be globally applicable, such as by way of examplewithout limitation, legal climate, gross domestic product (“GDP”)forecast, guarantor climate, collections efficiency, borrower groupcodes, and the like.

[0079] One method for sampling a portfolio includes searching among keyasset, borrower, and collateral characteristics for attributes whichheavily influence/generate risk. Table A below provides one example listof portfolio attributes in an asset valuation scenario. TABLE APortfolio attributes Borrower Size (by Borrower Group UPB) SecuredSyndicated (yes/no) Guaranteed Loan Type (Term, Revolving, etc.) % UPBfrom Liens in First Position Collection Score (0 = Bad, 1 = Good)12-month collections % of UPB % of Last Payment for Principal # BorrowerLoans Loan's portion of borrower UPB Single Family Residence ResidentialRetail Industrial Hospital Hospitality Multifamily LandDeveloped/Undeveloped/Other Office Stock/Margin Loans

[0080] Segmentation of the asset attributes is accomplished by encodingof attributes into “dummy variables”. For example, a common assetattribute is “Has borrower made a payment in the last 12 months?”, whichwould be encoded in a variable as a “1” if the answer is yes, and “0”otherwise. Similar “dummy variables” are used for other assetattributes.

[0081] The segmentation procedure is completed by using any statisticalprocedure which process the encoded asset attributes in such a way so asto segment the portfolio into groups of similar assets. One suchalgorithm is K-means clustering. In an example, where three assetattributes, Unpaid Principal Balance (UPB), Probability of Payment, ascale from 0 to 1; and Secured Score, a probability of being secured byreal estate collateral are used, the assets might be classified intofive groups with similar attributes.

[0082] Once the groupings of assets is made, the number of samples to betaken and submitted for further underwriting review is calculated byestablishing the confidence level with which statements can be madeabout the total recoveries in each segment (k), establishing theprecision with which one wishes to estimate the total recoveries in eachsegment (h) and providing an a priori estimate of the level and range ofrecoveries as a percentage of total Unpaid Principal Balance (UPB) (R),according to:${{Var}( {\hat{Y}}_{R} )} = {{n\quad\lbrack {1 - \frac{n}{N}} \rbrack} \times \frac{\lbrack {\underset{1}{\sum\limits^{N}}x_{i}} \rbrack^{2}}{\lbrack {\underset{1}{\sum\limits^{n}}x_{i}} \rbrack^{2}} \times \frac{\overset{N}{\sum\limits_{1}}( {y_{i} - {Rx}_{i}} )^{2}}{N - 1}}$

[0083] n=sample size

[0084] N=cluster size

[0085] x₁=UPB for sample i

[0086] y₁=recovery for sample i$R = {\frac{\underset{1}{\sum\limits^{N}}y_{i}}{\underset{1}{\sum\limits^{N}}x_{i}} = {{cluster}\quad {expected}\quad {recovery}\quad \%}}$

$\begin{matrix}{h^{2} = {k^{2} \times {n\quad\lbrack {1 - \frac{n}{N}} \rbrack} \times \frac{\lbrack {\sum\limits_{1}^{N}x_{i}} \rbrack^{2}}{\lbrack {\underset{1}{\sum\limits^{n}}x_{i}} \rbrack^{2}} \times \frac{\underset{l}{\sum\limits^{N}}( {y_{i} - {Rx}_{i}} )^{2}}{N - 1}}} & \text{(Equation~~C)}\end{matrix}$

[0087] h=error tolerance for estimating Y=$Y = {\sum\limits_{1}^{N}{y_{i}\quad {with}\quad {\hat{Y}}_{R}}}$

$\begin{matrix}\begin{matrix}{{\hat{Y}}_{R} = {{\hat{R} \times {\sum\limits_{i = 1}^{N}x_{i}}} = {\frac{\sum\limits_{i = 1}^{n}y_{i}}{\underset{1 = 1}{\sum\limits^{n}}x_{i}} \times {\underset{i = 1}{\sum\limits^{N}}x_{i}}}}} \\{= {\frac{\sum\limits_{i = 1}^{n}{\rho_{i}x_{i}}}{\underset{1 = 1}{\sum\limits^{n}}x_{i}} \times {\sum\limits_{i = 1}^{N}x_{i}}}}\end{matrix} & \text{(Equation~~D)}\end{matrix}$

[0088] k=constant in Tchebyshev's Formula:

|Ŷ_(R)−μ_(Ŷ) _(R) |≦k{square root}{square root over (Var(Ŷ)}_(R))

[0089] with probability ≧1−${{{\hat{Y}}_{R} - \mu_{{\hat{Y}}_{R}}}} \leq {k\sqrt{{Var}( {\hat{Y}}_{R} )}\quad {with}\quad {probability}}\quad \geq {1 - \frac{1}{k^{2}}}$

[0090] By solving Equation C for n, required sample size for the givencluster is obtained. Solving Equation C further allows the user tostate, with probability 1 $1 - \frac{1}{k^{2}}$

[0091] the calculated sample size, n, and associated underwritten valueswill estimate the total cluster recoveries to within an error of h,assuming that estimates of total segment recoveries are determined usingEquation D.

[0092] In practice, it is difficult to estimate variability in totalrecoveries without available data. A spreadsheet tool implements theabove by generating data in a Monte Carlo simulation, and guiding theuser through an analysis of the results until a favorable sample size isderived.

[0093] Table B provides an example output from a study of a group of 20loans, with estimated (expected) recoveries between 20% and 30% of UPB,and a range of UPB between 1 MM and 2 MM. Eight samples are needed toestimate the total recoveries for the 20 loans to within 10% of actual,with 75% confidence. TABLE B Sample Size Spreadsheet Wizard Sample ExpCume Cume Exp N n Size Rec Exp Rec UPB Rec % Residual (cluster size)(simple size) Expected Recovery 1 779,131 779,131 2,936,279 26 5% — 20 627.5% 2 716,951 1,496,082 5,447,631 27 5% 27,259 Face Range ER % RangeFace Value 3 359,327 1,855,409 6,702,090 27 7% 12,042 2,000,000 5.0%44,160,329 4 481,798 2,337,206 8,538,875 27 4% (20,956) Min Face Min ER% Expected Recovery 5 606,774 2,943,980 10,706,452 27 5% 10,7501,000,000 25.0% 12,123,821 6 418,899 3,362,880 12,207,495 27 5% 5,397Confidence k Precision Precision % 7 622,516 3,985,396 14,609,180 27 3%(32,665) 75 0% 2.00 1,212,382 10.0% 8 594,799 4,580,195 16,911,278 27 1%(28,694) 9 713,922 5,294,117 19,440,132 27 2% 25,241 10 494,2305,788,346 21,153,615 27 4% 25,363 11 735,334 6,523,680 24,031,814 27 1%(45,983) 12 683,155 7,206,835 26,387,193 27 3% 39,857 13 748,4137,955,248 29,256,251 27 2% (31,730) 14 419,885 8,375,133 30,726,773 273% 19,068 15 757,050 9,132,183 33,682,971 27 1% (44,439) 16 553,6749,685,857 35,690,262 27 1% 8,922 17 761,579 10,447,435 38,234,459 27 3%66,386 18 677,811 11,125,246 40,756,944 27 3% (10,741) 19 563,81111,689,057 42,688,952 27 4% 34,790 20 434,763 12,123,821 44,160,329 275% 30,810

[0094] The appropriate variance adjusted forecast is made for each assetand the valuation tables are constructed to include every asset in theportfolio. The recovery is valued with continuous probabilities at theunit of sale, which in one embodiment is a tranche. In the use of system28, internal rate of return (“IRR”) and variance would then be assessed.Preferred tranches have lower variances for a given IRR. The probabilityof each tranche's net present value (“NPV”) to be above 0 is assessedusing the project's discount rate. A discount rate is determined fromthe opportunity cost of capital, plus FX swap cost, plus risks ingeneral uncertainties inherent in the variances of forecasted cash flowrecovery. If it appears that there is more than a five-percent certaintythat the project will have a negative NPV, no bid is made. Dealevaluation is by tranche with decision criteria being IRR, risk varianceof the IRR in a tranche, estimated willingness and ability of thetranche to pay, time to profit (“TPP”) and the risk variance in thepayback by tranche, and NPV of the expected cash flow by tranchediscounted to risk free rate.

[0095] In competitive bid circumstances when the content of assetportfolios is not negotiable, the investor or seller has a strongfinancial incentive to select only the portions of total assetsavailable for transaction that will give their aggregated financialstructure the best risk/return. Meeting minimum risk/return expectedvalues with assets that will have a higher probability of maximum upsideprobabilities is even more attractive to investors.

[0096] The aggregated portfolio is divided into separately marketablesub portfolios or tranches. Each tranch has a forecasted cash flowprobability distribution and time duration from prior analytics. Thesetranches are then given a trial price. The new assets are combined withthe existing asset performance of the selling or buying party andsubjected to Monte Carlo case generation (with associated crosscorrelations accounted for).

[0097] The tranch selection process includes a random selection oftrances not to buy. Once the portfolio effects take on a pattern, thebest selection of tranches to purchase, at what price, subject toconstraints is found by stochastic optimization.

[0098] Using NPV can be misleading due to the effects associated withdouble discounting which will occur when pessimistic case scenarios arediscounted to obtain PV. Using time to profit is used to overcome thislimitation and the marginal capital cost or risk free rate is used inthe discounting as determined by analysts conducting the evaluation.

[0099] Supervised learning process 206 of inferred valuation procedure40 and steps 120, 122 and 126 of partial sampling procedure 108 havesubstantial similarity in that the underwriter is actively involved inthe process, but the process is automated. FIG. 6 is a flow diagramillustrating a process 210 for automated underwriting of segmentablefinancial instrument assets. First clusters of financial instruments aredefined 212 by common attributes. An expert opinion 214 of value isgiven for selected samples from the defined clusters based upon theattributes. This opinion is used in a sample underwriting process 216and values are checked for combinations of attributes and reconciled218. Process 210 then selects and sets 220 the individual attributes tobe used and then classifies 222 individual assets into clusters. Clustervaluation is applied 224 to each cluster asset. Using the clustervaluation, the values are desegregated by a rule 226 to create a creditanalyst table 228.

[0100]FIG. 7 is a flow diagram of one exemplary embodiment ofunsupervised leaming 208 that includes several modules. A dataacquisition module 230 collects relevant data 78 wherever available. Avariable selection module 232 identifies the asset relevant variablesdeemed critical by credit review or with the most discriminate power inseparating various asset groups. A hierarchical segmentation module 234segments the entire portfolio of assets into bins based on criticalvariables selected by analysts. A FCM module 236 further classifies eachbin into clusters based on natural structure of the asset data. Anunderwriting review module 238 assigns projected cash flow and riskscores 138 (shown in FIG. 3) to each cluster. This score is thensupplied to the individual asset values in credit analyst table 136 forthe assets from the clusters being adjusted in procedure 40 to produceadjusted credit analyst table 140. The process is iterative andcontinuous and can be performed by computer so that it continues whilestandard underwriting is being performed elsewhere.

[0101]FIG. 8 illustrates an alternate exemplary inferred valuationprocess 240 used in place of the process described in FIGS. 3 and 4. Inalternate process 240, a seven-step process is used to rapidly value areal estate loan portfolio using a combination of full underwriting,partial underwriting and inferred valuation. First, assets are sampled242 according to risk. Second, assets are underwritten 244, andvaluations recorded. Third, market value clusters are formed 246, suchas by FCM, as described below. Fourth, regression models are built 248,for the underwritten assets. A best model is selected 250, for theunderwritten assets from among those built 248 earlier. Sixth, thecounts for the selected models are calculated 252. Seventh, models areapplied 254, as selected 250 to non-underwritten or inferentially valuedportion 42 of portfolio 12 in a manner weighted by the counts to predictindividual values for each of the non-underwritten assets. Theindividual asset values produced according to process 240 are thenplaced in adjusted credit analyst table 140 (see FIG. 3).

[0102] In sampling assets 242, underwriters use stratified randomsampling to select assets for detailed review. Strata are constructedfrom collateral attributes. Examples of collateral attributes for realestate portfolios include, collateral usage (commercial or residential),previous appraisal amount, market value cluster (predicted from previousappraisal amount, land area, building area, current appraisal amount,court auction realized price, property type and property location.Typically, assets are sampled in an adverse manner, i.e., purposelyselected from a list ordered by decreasing Unpaid Principal Balance(“UPB”) or Previous Appraisal Amount (“PAA”).

[0103] Underwriting 244 is a largely manual process in which expertunderwriters ascribe a notion of worth to collateral assets. Theunderwritten valuations are stored in a master database table, such asdatabase 76 (shown in FIG. 2). Valuations are typically summarized interms of monetary units (e.g., 100,000 KRW), at then current marketprices.

[0104]FIG. 9 is a high level overview 290 of the automated portion ofthe process employed by system 28. Automated procedures are used byunderwriters to assist in full underwriting based on procedure 34 (seealso FIG. 3). Knowledge captured in procedure 34 is applied in inferredvaluation procedure 40 to reduce cost and uncertainty in due diligencevaluations of financial instruments and to reduce cost and variabilitybetween due diligence valuations. The valuations are subjected to a cashflow model which includes asset level valuation 146, deterministic cashflow bridge 148, stochastic cash flow bridge 152 and cash flow table150. The resultant bid valuation 154 is subjected to gaming strategies160 and management adjustments 162 to produce the final bid 164.

[0105]FIG. 10 is a flow diagram of an exemplary embodiment of formingclusters 246. In forming clusters 246, underwriters, with the aid ofalgorithms, such as for example algorithms 134 (shown in FIG. 3) performan analysis using a classification and regression tree based model,which results in a grouping of UW assets by Collateral Usage and MarketValue (“CUMV”) groups, using Previous Appraisal Amount (“PAA”) as thedriving variable.

[0106] Two approaches to assess the performance of a classification andregression tree based model are outlined below. One approach utilizes aratio of the sum of squared error (SSE) of a classification andregression tree based approach to that of a simple model, called anerror ratio. A simple model is a model which assigns an average assetprice to all assets. The second approach computes a coefficient ofdetermination, denoted as R², and defined as

[0107] R²=1−(SSE/SST), where SST is a sum of squares total.

[0108] R² is the contribution of a single asset within each segmentrelative to the entire population, a higher R² value for an asset withina particular segment, the higher is the contribution. The differentportfolio segments are ranked based on the two approaches giving anindication of how good the predictive capabilities of the model arewithin each portfolio segment, giving a comfort level to the bidder interms of pricing, for example, each tranche. TABLE C Rank Error Ratiosand R² value per asset Rank Error R-Squared pe Ratio for Loan for CTranche CO Data B C Grand Total C loans loans CO 01 Sum of a Curr UPBTHB 645,959,109 82,692,009 728,651,119 Count of Loan No 66 10 76 Sum ofSST 599,959,990,091,044 72331,126,127,460 872,301,116,218,504 Sum ofSSE(CART) 252,088,256,587,362 26,877,527,094,886 278,965,783,682,227 Sumof SSE(Simple) 440,700,263,795,025 35,037006,655,009 477,337,270,451,0340.733617 0.18% CO 02 Sum of a Curr UPB THB 58,779,400 379,765,147438,544,547 Count of Loan No 9 118 127 Sum of SST 32,332,549,696,1331,039,401,135,208,180 1,071,733,684,904,320 Sum of SSE(CART)6,139,033,273,655 83,849,226,818,428 89,989,160,092,084 Sum ofSSE(Simple) 7,037,799,486,366 136,366,441,963,041 143,404,241,449,4090.614882 0.06% CO 03 Sum of a Curr UPB THB 798,969,257 276,915,5731,075,884,830 Count of Loan No 98 99 197 Sum of SST2,869,807,879,172,570 1,017,087,163,438,760 3,886,895,042,611,430 Sum ofSSE(CART) 729,304,505,050,836 65,902,258,632,574 795,206,763,683,411 Sumof SSE(Simple) 929,822,648,064,557 41,730,444,375,417971,553,092,439,966 1.579237 0.46% CO 04 Sum of a Curr UPB THB916,281,888 184,828,399 1,101,110,287 Count of Loan No 116 28 144 Sum ofSST 927,232,177,539,735 223,991,862,418,473 1,151,224,039,958,210 Sum ofSSE(CART) 329,865,566,636,764 92,347,778,018,417 422,217,344,655,182 Sumof SSE(Simple) 688,543,329,448,792 62,722,788,732,158751,266,118,230,950 1.472316 0.11% CO 05 Sum of a Curr UPB THB221,759,281 41,505,412 263,274,692 Count of Loan No 36 19 55 Sum of SST270,033,444,922,605 164,501,058,694,453 434,634,503,617,058 Sum ofSSE(CART) 28,547,982,198,098 10,191,006,095,769 38,738,988,293,867 Sumof SSE(Simple) 28,897,015,065,018 8,519,509,247,449 37,416,524,313,3671.196196 0.14% Total Sum of a Curr 2,641,758,934 965,706,5403,607,465,475 UPB THB Total Count of Loan 325 274 599 No Total Sum ofSST 4,699,376,041,422,190 2,517,412,345,887,330 7,216,788,387,309,520Total Sum of SSE 1,345,950,243,746,720 279,167,796,660,0541,625,118,040,406,770 (CART) Total Sum of SSE 2,095,001,055,880,660285,976,191,024,073 2,380,977,246,884,730 0.976192 0.22% (Simple)R-Squared (CART) 71.4% 88.9% 77.5% R-Squared (Simple) 55.4% 88.6% 67.0%

[0109] A first step is to define relevant portfolio segmentations. Thesegmentations could be pre-defined tranches, for example, based onindustry, Unpaid Balance (UPB) amounts, region or customer risk. Table Cabove is an example of defined segments based on tranches and assetrankings (B or C).

[0110] Table C provides an example output from a study of a portfoliowith five tranches and two different asset types (B and C). The tableshows how the error ratio is ranked for the different segments. Also,the R² values for each asset are also computed for assets of type Cwithin each segment.

[0111] A second step is to compute SSE values for each portfolio segmentof interest for the classification and regression tree model and for thesimple model (extrapolation of an average price). An error ratio iscomputed from the SSE based on the classification and regression treemodel divided by an SSE based on the simple model. If the error ratio isless than one, then the classification and regression tree based modelis a better predictor than the simple model. As an added benefit, asuperior model can be assembled as a “hybrid” combination of theclassification and regression tree and simple models, by choosing themodel which performs best in each segment, according to the error ratiometric.

[0112] A third step is to compute R² values for each asset within eachportfolio segment. R² per asset is computed as (SST per segment−SSE persegment)/(overall SST for all assets×number of assets within eachsegment).

[0113] Lastly all the segments are ranked based on the error ratiocomputed in the second step and the R² values computed in the thirdstep. The model is accurate in predicting price values for segments thatrank high on both of the two metrics, the error ratio and R² andsuperior models are assembled using these metrics.

[0114] Table D shows the relative ranking of the five tranches for theassets of type C (from Table C) on the basis of the two performancemetrics. TABLE D Portfolio Segment Ranking Tranche CO C R-Squared RankError Ratio Rank R-squared CO 01 0.73 0.18% 2 2 CO 02 0.61 0.06% 1 5 CO03 1.58 0.46% 5 1 CO 04 1.47 0.11% 4 4 CO 05 1.20 0.14% 3 3

[0115]FIG. 10 is a flow diagram illustrating an exemplary embodiment offorming clusters 246 using FCM to choose clusters for modeling. Computer38 (shown in FIG. 2) forms clusters 246 by taking selected data 78 andperforming FCM analysis to produce the clusters.

[0116]FIG. 11 illustrates building models 248, selecting best models 250and calculating counts 252 in which six models are built using database76. Computer 38 (shown in FIG. 3) performs this process. Model building248 is used to assist the underwriter in prioritizing assets for fullunderwriting 14 and sample-based underwriting 34, as well as forinferential valuation.

[0117] The lower portion of FIG. 11 is a table illustrating an exemplaryembodiment of selecting best models 250 from six models built inaccordance with building models 248 d. The models differ according towhich variables are used as X's. All models use CUMV Cluster (these arepresent for all assets). The models from building models 248 are used topredict Court Auction Value (“CAV”) 256 in addition to Market Value(“MAV”) 258. Other embodiments (not shown) use other models to predictother values

[0118] In selecting best models 250, the best models of K regressionmodels under consideration (here, K=6), are selected. The best model ischosen for each UW asset, according to the following metric:min/k{abs(y−ŷ_(k)),1E⁹⁹}, where y is the UW value to be predicted, andŷ_(k) is a prediction from the k^(th) regression model, for k=1, 2, . .. , K.

[0119] In calculating counts 252, the number of times each of the Kmodels is selected within each CUMV cluster is counted. FIG. 11 containsthese counts for CAV and MAV modeling scenarios. Other modelingscenarios are used in other embodiments.

[0120] When applying models 254, the weighted average prediction fromall models that yielded a prediction for each non-UW asset is used. Theweights are constructed from the frequencies of the counts calculated252, and the predictions come from the modeling process. In oneembodiment, a commercial statistical analysis software system is used toproduce the models. An artifact of using the statistical analysissoftware system is that each non-UW asset will get a predicted UW valuefrom each model for which the non-UW asset has each input variable,i.e., “X variable” present. Other modeling packages share this trait.)Equation E below details the procedure. $\begin{matrix}{\overset{\hat{\_}}{y} = \frac{\sum\limits_{i,j,k}{I_{lk}f_{ijk}{\hat{y}}_{lk}}}{\sum\limits_{i,j,k}{I_{lk}f_{ijk}}}} & \text{(Equation~~E)}\end{matrix}$

[0121] In Equation C, I_(lk)=1 if model k produced a prediction forasset l, and is zero otherwise; f_(ijk)=count of times model k wasselected for UW assets among the i^(th) CUMV type (i=1,2), and thej^(th) CUMV cluster (j=1, 2, 3); and ŷ_(lk)=prediction for y_(l) frommodel k. Note there is only a contribution from each modeling approachfor which an asset has a prediction, with each being weighted by thenumber of times the modeling approach was selected for all UW assets ofthe same CUMV cluster.

[0122] Process 240 is also used to estimate a Lower Confidence Limit(“LCL”) and Upper Confidence Limit (“UCL”) for the mean prediction, witha substitution of the corresponding statistic for ŷ_(lk) in Equation E.

[0123] Referring back again to FIG. 3, supervised learning process 206and unsupervised learning process 208 use clustering. “Clustering” is atool that attempts to assess the relationships among patterns of thedata set by organizing the patterns into groups or clusters such thatpatterns within a cluster are more similar to each other than arepatterns belonging to different clusters. That is, the purpose ofclustering is to distill natural groupings of data from a large dataset, producing a concise representation of a system's behavior.Unsupervised learning step 208, employs a fuzzy clustering method(“FCM”) and knowledge engineering to group assets automatically forvaluation. FCM is a known method that has been widely used and appliedin statistical modeling. The method aims at minimizing intra-clusterdistance and maximizing inter-cluster distance. Typically the Euclideandistance is used.

[0124] FCM 248 (see FIG. 10) at the same time minimizes theintra-cluster distance and maximizes the inter-cluster distance.Typically the Euclidean distance is used. FCM is an iterativeoptimization algorithm that minimizes the cost function $\begin{matrix}{J = {\sum\limits_{k = 1}^{n}{\sum\limits_{i = 1}^{c}{\mu_{ik}^{m}{{X_{k} - V_{i}}}^{2}}}}} & ( {{Equation}\quad F} )\end{matrix}$

[0125] where n is the number of data points; c is the number ofclusters, X_(k) is the k^(th) data point; V_(i) is the i^(th) clustercentroid; μ_(ik) is the degree of membership of the k^(th) data in thei^(th) cluster; m is a constant greater than 1 (typically m=2). Notethat μ_(lk) is a real number and bounded in [0,1]. μ_(ik)=1 means thati^(th) data is definitely in k^(th) cluster, while μ_(ik)=0 means thati^(th) data is definitely not in k^(th) cluster. If μ_(ik)=0.5, then itmeans that i^(th) data is partially in k^(th) cluster to the degree 0.5.Intuitively, the cost function would be minimized if each data pointbelongs exactly to a specific cluster and there is no partial degree ofmembership to any other clusters. That is, there is no ambiguity inassigning each data point to the cluster to which it belongs.

[0126] The degree of membership μ_(ik) is defined by $\begin{matrix}{\mu_{ik} = \frac{1}{\sum\limits_{j = 1}^{c}( \frac{{X_{k} - V_{i}}}{{{X_{k} - V_{j}}}^{2}} )^{\frac{1}{m - 1}}}} & ( {{Equation}\quad G} )\end{matrix}$

[0127] Intuitively, μ_(ik), the degree of membership of the data pointX_(k) in the cluster centroid V_(i), increases as X_(k) is gettingcloser to V_(i). At the same time, μ_(ik) would get smaller as X_(k) isgetting farther away V_(j) (other clusters).

[0128] The i^(th) cluster centroid V_(l) is defined by $\begin{matrix}{V_{i} = \frac{\sum\limits_{k = 1}^{n}{( \mu_{ik} )^{m}X_{k}}}{\sum\limits_{k = 1}^{n}( \mu_{ik} )^{m}}} & ( {{Equation}\quad H} )\end{matrix}$

[0129] Intuitively, V_(i), the i^(th) cluster centroid, is the weightedsum of the coordinates of X_(k), where k is the number of data points.

[0130] Starting with a desired number of clusters c and an initialestimate for each cluster center V_(i), i=1, 2, . . . , c, FCM willconverge to a solution for V_(i) that represents either a local minimumor a saddle point of the cost function. The quality of the FCM solution,like that of most nonlinear optimization problems, depends strongly onthe choice of initial values—the number c and the initial clustercentroids V_(i)).

[0131] In one exemplary embodiment, the entire portfolio 12 is segmentedby unsupervised fuzzy clustering and each cluster is reviewed byunder-writing experts. thereby assisting the underwriters in choosingthe financial instruments for full underwriting 14 and sampleunderwriting 34. Alternatively, this FCM can be applied just to portion42. As a result, each cluster gets assigned a HELTR composite score forpurposes of adjustment 138 (see FIG. 3) In essence, the HELTR compositescore captures both expected and range of cash flow, its timing and therisk associated with each cluster.

[0132] Referring now to FIG. 2, the ratio of full underwrite portion 16to the total portfolio 12 is in one exemplary embodiment 25% of theassets and 60% of the face value of all assets. Full underwriting ofthese assets is warranted due to their size and value. However, thisunderwriting is fairly uniform for all underwriters, so the underwritingis not likely to produce significant bidding variances. The remaining40%, however, comprising portions 36 and 42, which in the exemplaryembodiment constitute 75% of the assets but only 40% of the face valueare highly speculative until underwritten. To the extent value can befound in portions 36 and 42 f, for example without limitation, anadditional five percent over gross extrapolation, the difference meaningthe difference between winning and losing the entire portfolio bid orthe entire tranche bid meaning hundreds of millions of dollarsdifference in profit.

[0133] In the case of insurance policies, in accordance with procedure40, statistics are used in an attempt to answer three basic questions:(a) How should we collect our data? (b) How should we summarize the datawe collected? And (c) How accurate are our data summaries? Algorithm 134answers question (c), and is a computer-based method without complicatedtheoretical proofs. Algorithm 134 for insurance policy inferentialvaluations is suitable for answering statistical inferences that are toocomplicated for traditional statistical analysis. Algorithm 134 forinsurance policy valuation simulates the distribution of statisticalestimates by repeatedly sampling with replacement. The algorithmgenerally is composed of three main steps: (I) Sampling withreplacement, (II) Evaluating statistics of interest, and (III)Estimating standard deviation.

[0134] In accordance with insurance algorithm 134, estimates of NPVstandard error are performed as follows. For each of the risk models andfor each segment in the models, assuming there are N policies in thesegment, n samples are selected using sampling with replacement (forexample, n=100). Each sample contains N policies, too, in this example.For each sample, and for all historical policies: $\begin{matrix}{\frac{A}{E_{w}} = \frac{\sum({Act})}{\frac{\sum({Wtdexp})}{0.72858}}} & ( {{Equation}\quad I} )\end{matrix}$

[0135] Next, net present value is generated by $\begin{matrix}{{NPV} = {{\sum P} - {\sum E} - {( {\sum C} ) \times \frac{A}{E_{w}}}}} & ( {{Equation}\quad J} )\end{matrix}$

[0136] for recent policies. Compute the sample standard deviation forthe n NPV values. In Equation I, Act is the actual claim and Wtdexp isthe weighted expected claim for each individual policy.

[0137]FIG. 12 is a table of exemplary criteria 80 and exemplary rulesets for credit scoring 138. Other criteria could be selected dependingon the type of financial instrument and particular bidding conditions orany other desires or preferences of the bidder.

[0138]FIG. 13 is a more detailed tree chart diagram 260 similar to treechart 66 (see lower portion of FIG. 2). In FIG. 13, the segregation isby (a) whether secured, (b) whether revolving, (c) whether the lastpayment was zero. The result is six clusters 262, 264, 266, 268 270,272, casually known as a “shaker tree”.

[0139]FIG. 14 illustrates an exemplary system 300 in accordance with oneembodiment of the present invention. System 300 includes at least onecomputer configured as a server 302 and a plurality of other computers304 coupled to server 302 to form a network. In one embodiment,computers 304 are client systems including a web browser, and server 302is accessible to computers 304 via the Internet. In addition, server 302is a computer. Computers 304 are interconnected to the Internet throughmany interfaces including a network, such as a local area network (LAN)or a wide area network (WAN), dial-in-connections, cable modems andspecial high-speed ISDN lines. Computers 304 could be any device capableof interconnecting to the Internet including a web-based phone or otherweb-based connectable equipment, including wireless web and satellite.Server 302 includes a database server 306 connected to a centralizeddatabase 76 (also shown in FIG. 2) which contains data describing setsof asset portfolios. In one embodiment, centralized database 76 isstored on database server 306 and is accessed by users at one ofcomputers 304 by logging onto server sub-system 302 through one ofcomputers 304. In an alternative embodiment centralized database 76 isstored remotely from server 302. Server 302 is further configured toreceive and store information for the asset valuation methods describedabove.

[0140] While system 300 is described as a networked system, it iscontemplated that the methods and algorithms described herein forexamination and manipulation of asset portfolios are capable of beingimplemented in a stand-alone computer system that is not networked toother computers.

[0141] While the invention has been described in terms of variousspecific embodiments, those skilled in the art will recognize that theinvention can be practiced with modification within the spirit and scopeof the claims.

What is claimed is:
 1. A method for grouping assets using aclassification and regression tree based model of asset portfolios, saidmethod comprising the steps of: defining relevant portfoliosegmentations; assessing performance of the classification andregression tree based model against a simple model; and ranking allportfolio segments based upon performance of the models.
 2. A methodaccording to claim 1 wherein said step of defining relevant portfoliosegmentations further comprises the step of selecting segmentationsbased upon at least one of pre-defined tranches, unpaid balance amounts,region customer risk and loan rankings.
 3. A method according to claim 1wherein said step of assessing performance of the classification andregression tree based model against a simple model further comprises thestep of computing sum of squared error (SSE) values for each relevantportfolio segment using a classification and regression tree based modeland a simple model.
 4. A method according to claim 3 wherein said stepof computing sum of squared error (SSE) values for each relevantportfolio segment using a classification and regression tree based modeland a simple model further comprises the step of assigning an averageasset price to all assets for the simple model.
 5. A method according toclaim 3 wherein said step of computing sum of squared error (SSE) valuesfor each relevant portfolio segment further comprises the step ofcomputing an error ratio of the classification and regression tree basedmodel to the simple model.
 6. A method according to claim 3 furthercomprising the step of using the classification and regression treebased model as a predictor if the error ratio is less than one.
 7. Amethod according to claim 1 wherein said step of assessing performanceof the classification and regression tree based model against a simplemodel further comprises the step of computing R² values for each assetwithin each portfolio segment, where R² per asset is computed as (sum ofsquares total (SST) per segment−SSE per segment)/(overall SST for allassets×number of assets within each segment).
 8. A method according toclaim 5 wherein said step of ranking all portfolio segments based uponperformance of the models further comprises the step of rankingportfolio segments based on the computed error ratio.
 9. A methodaccording to claim 1 wherein said step of ranking all portfolio segmentsbased upon performance of the models further comprises the step ofranking portfolio segments based upon a coefficient of determination.10. A method according to claim 8 where the coefficient ofdetermination, R²=1−(sum of squared error/sum of squares total).
 11. Asystem for grouping assets using a classification and regression treebased model of asset portfolios comprising: a computer configured as aserver and a database of asset portfolios; at least one client systemconnected to said server through a network, said server configured touse a classification and regression tree based model to define relevantportfolio segmentations, assess performance of the classification andregression tree based model against a simple model and rank allportfolio segments based upon performance of the classification andregression tree based model and the simple model.
 12. A system accordingto claim 11 wherein said server is configured to select segmentationsbased upon at least one of pre-defined tranches, unpaid balance amounts,region customer risk and asset rankings.
 13. A system according to claim11 wherein said server is configured to compute a sum of squared error(SSE) values for each relevant portfolio segment using a classificationand regression tree based model and a simple model.
 14. A systemaccording to claim 13 wherein said server is configured with a simplemodel that is configured to assign an average asset price to all assets.15. A system according to claim 13 wherein said server is configured tocompute an error ratio of the classification and regression tree basedmodel to the simple model.
 16. A system according to claim 13 whereinsaid server is configured to use the classification and regression treebased model as a predictor if the error ratio is less than one.
 17. Asystem according to claim 11 wherein said server is configured tocompute R² values for each asset within each portfolio segment, where R²per asset is computed as (sum of squares total (SST) per segment−SSE persegment)/(overall SST for all assets×number of assets within eachsegment).
 18. A system according to claim 15 wherein said server isconfigured to rank portfolio segments based on the computed error ratio.19. A system according to claim 11 wherein said server is configured torank portfolio segments based upon a coefficient of determination.
 20. Asystem according to claim 19 wherein said coefficient of determinationis R²=1−(SSE/SST).
 21. A computer configured for grouping assets using aclassification and regression tree based model of asset portfolios, saidcomputer including a database of asset portfolios, said computerprogrammed to: use a classification and regression tree based model todefine relevant portfolio segmentations; assess performance of theclassification and regression tree based model against a simple model;and rank all portfolio segments based upon performance of theclassification and regression tree based model and the simple model. 22.A computer according to claim 21 programmed to select segmentationsbased upon at least one of pre-defined tranches, unpaid balance amounts,region customer risk and asset rankings.
 23. A computer according toclaim 21 programmed to compute a sum of squared error (SSE) values foreach relevant portfolio segment using a classification and regressiontree based model and a simple model.
 24. A computer according to claim23 programmed with a simple model that is configured to assign anaverage asset price to all assets.
 25. A computer according to claim 23programmed to compute an error ratio of the classification andregression tree based model to the simple model.
 26. A computeraccording to claim 23 programmed to use the classification andregression tree based model as a predictor if the error ratio is lessthan one.
 27. A computer according to claim 21 programmed to compute R²values for each asset within each portfolio segment, where R² per assetis computed as (sum of squares total (SST) per segment−SSE persegment)/(overall SST for all assets×number of assets within eachsegment).
 28. A computer according to claim 25 programmed to rankportfolio segments based on the computed error ratio.
 29. A computeraccording to claim 21 programmed to rank portfolio segments based upon acoefficient of determination.
 30. A computer according to claim 29wherein said coefficient of determination is R₂=1−(SSE/SST).